Automatic anterior chamber angle structure segmentation in AS-OCT image based on label transfer

Abstract

The anterior chamber angle (ACA) plays an important role for diagnosis and treatment of angle-closure glaucoma. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging is qualitative and quantitative assessment for the ACA structure. In this paper, we propose a novel fully automatic segmentation method for anterior chamber angle structure in AS-OCT. In our method, the initial labels are obtained by using label transfer from the AS-OCT reference dataset. Then, these labels are refined and utilized as the landmarks to support the structure segmentation. Finally, the major clinical structures: corneal boundary, iris region, and trabecular-iris contact, are extracted as the segmentation result. Experiments show that our proposed method achieve the satisfactory segmentation performance on the clinical AS-OCT dataset. Our proposed method has potential in the applications of clinical ACA parameter measurement and automatic glaucoma classification.